1. Field of the Invention
This disclosure relates to positioning systems and, more particularly, to the computation of position solutions for mobile receivers.
2. Background of the Invention
The Global Positioning System (GPS) is a satellite navigation system, or satellite positioning system, designed to provide position, velocity and time information almost anywhere in the world. GPS was developed by the Unites States Department of Defense, and currently includes a constellation of twenty-four operational satellites. Other types of satellite navigation systems include the Wide Area Augmentation System (WAAS), the Global Navigation Satellite System (GLONASS) deployed by the Russian Federation, and the Galileo system planned by the European Union. As used herein, “satellite positioning system” (SPS) will be understood to refer to GPS, Galileo, GLONASS, NAVSTAR, GNSS, a system that uses satellites from a combination of these systems, pseudolite systems, or any SPS developed in the future. A pseudolite system (from pseudo-satellite) refers to a non-satellite system that performs or appears as an SPS satellite system, such as by using ground-based transmitter to create GPS signals.
A variety of receivers have been designed to decode the signals transmitted from the satellites to determine position, velocity or time. In general, to decipher the signals and compute a final position, a receiver must acquire signals from satellites in view, measure and track the received signals, and recover navigational data from the signals. By accurately measuring the distance from three different satellites, the receiver triangulates its position (i.e., solves for a latitude, longitude and altitude). In particular, the receiver computes distances to satellites by measuring the time required for each signal to travel from the respective satellite to the receiver. This computation requires precise time information. For this reason, measurements from a fourth satellite help to resolve time and measurement errors (e.g., errors created by inaccuracies of timing circuits within the receiver).
In certain locations (e.g., urban environments with tall buildings), a receiver may be able to acquire signals from only three or fewer satellites. In these situations, the receiver will be unable to resolve all four variables of the position solution: latitude, longitude, altitude and time. If the receiver is able to acquire signals from only three satellites, the receiver may forego an altitude calculation to resolve latitude, longitude and time. Alternately, if altitude is obtained via an alternate means, all four variables may be resolved from three satellite signals. If fewer than three signals are available, the receiver may be unable to calculate its position.
To address this limitation, some receivers employ a hybrid location technology. These hybrid receivers utilize signals from both the base stations and available signals from GPS satellites to resolve the position and time variables. As with satellite signals, a hybrid-location receiver measures the time delay of a wireless signal from a base station in order to compute a distance to that base station. This hybrid-location technique often allows a receiver to compute a position solution in a wide variety of locations where conventional positioning techniques using only satellite signals would otherwise fail. In code division multiple access (CDMA) mobile wireless systems, for example, this hybrid technique of measuring time delays and computing distances to both base stations and satellites is referred to as Advanced Forward Link Trilateration (AFLT).
Clock precision and accuracy within a receiver affects the resulting accuracy of a location solution. In synchronized systems, such as existing CDMA systems, the timing information communicated by a cellular base stations is synchronized with the timing information from the GPS satellites, which allows precise time to be available throughout the system. In some systems, such as the Global System for Mobile Communications (GSM) cellular system, timing information is not synchronized between the base stations and the GPS satellites. In these systems, Location Measurement Units (LMUs) are added to the existing infrastructure to provide precise timing information for the wireless network.
To determine a current position of a mobile station, motion of the mobile station may be modeled (using, for example, past positional measurements) in order to estimate the current velocity (or a range of potential velocities) of the mobile station. Map information, which places the mobile station on a street or highway, which is oriented in a known direction, may also be used to model the current velocity of the mobile station. Filtering methods can further enhance this trajectory estimation technique. A Kalman filter is one filtering method that adaptively tracks the mobile station's trajectory to predict its dynamic state in terms of speed and position. The Kalman filter recursively finds solutions for a least-squares problem and may be used to estimation of past, present, and even future positions. A Kalman filter, extended Kalman filter, or other least-mean-square filter is also useful when a model of user movement is uncertain.
Some position determining systems use a positioning filter, such as a Kalman filter, For example, U.S. Publ. No. 2007/0205941 (published Sep. 6, 2008 and titled “Method for position determination with measurement stitching” to Riley, which is incorporated by reference herein) describes determining a position estimate of a mobile communication device based on an updated positioning filter.
A Kalman filter (KF) is an optimal recursive data estimation algorithm. It is frequently used to model attributes of moving entities such as aircraft, people, vehicles, etc. These attributes can include acceleration, velocity and/or position. The positioning filter algorithm uses a current state of the system and a current measurement to estimate a new state of the system. In practice, a Kalman filter combines all available measurement data and prior knowledge about the system, measuring devices, and error statistics to produce an estimate of the desired variables in such a manner that the error is statistically minimized.
In some positioning algorithms, a mobile station determines its position by modeling an expected motion of the mobile station. A positioning filter, such as a Kalman filter, may assume a movement parameter as constant. For example, the movement parameter may set the user velocity, user speed or user turn radius to a constant value. These models may allow deviation from an expected motion model. In the case of a Kalman filter, the expected amount of deviation from the expected motion model is enabled via process noise. Similarly, position domain filters may have some estimated change of user velocity as well. Unfortunately, the choice of how much variation from the expected user motion model to use, and even what user motion model to use often must be made well in advance, and without any external knowledge of the actual user motion, aside from the GPS measurements themselves. This leaves the models somewhat conservative and sub-optimal.
Accordingly, a need remains to improve the position determining capabilities of mobile communications devices and to do so in a timely and efficient way.